Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
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Book Description
Machine Learning, represents an ultimate new era in software development enabling computers, mobiles and other devices to complete critical tasks without any special programming, thus allowing smartphones to produce an enormous amount useful data that can be mined analyzed and used to make predictions in the field of machine learning. This book will help you with how to deal with machine learning on mobile with easy to follow practical examples.
The book begins with giving you an introduction to machine learning on mobile and provides useful insights to be comfortable with the subject. You will then dive deep into supervised and unsupervised learning on mobile. Within this section, the book would cover important machine learning tools for mobile devices such as clustering, classification, regression followed by popular algorithms – Naive Bayes and Logistic Regression. You will also get to learn how to build a machine learning model using mobile-based libraries such as CoreML, Caffe2Go, Tensorflow lite and Weka on Android and iOS platform using SDKs. Next, you get to understand machine learning on cloud and how cloud services for machine learning are used in mobiles. Finally, the book would also cover an experiment on performing on-device image classification using mobile-based Tensorflow Lite and caffe2Go framework helping you to get a thorough understanding in building an artificial intelligence engine that runs directly on mobile devices.
By the end of this book, you would get a thorough understanding of machine learning models, performing on-device machine learning thereby enabling you to run artificial intelligence in real-time on mobile devices.
